03Service

AI & Automation

Intelligent systems that learn, adapt, and create real leverage.

Start a project
AI Pipeline · Running
Ingestion100%
Embedding100%
Retrieval78%
Generation32%
Output

AI is only valuable when it solves a real problem. We don't build AI for its own sake — we identify the workflows, decisions, and bottlenecks in your business where intelligence creates measurable leverage, then engineer systems that deliver it reliably. From LLM integrations to custom ML pipelines, we build AI that works in production.

What's included

How we deliver it

LLM integration & orchestration

Production-grade integrations with OpenAI, Anthropic, and open-source models. We handle prompt engineering, context management, token optimisation, and fallback strategies.

Retrieval-augmented generation (RAG)

AI that knows your data. We build RAG pipelines that ground LLM responses in your documents, knowledge bases, and databases — reducing hallucinations and increasing accuracy.

Intelligent automation

Workflows that used to require human judgment, automated with AI. Document processing, classification, extraction, routing — built to run at scale without supervision.

AI agents & tool use

Autonomous agents that can browse, search, write, and execute — built with LangChain or custom orchestration frameworks, with guardrails and human-in-the-loop controls.

Anomaly detection & forecasting

Custom ML models for time-series forecasting, anomaly detection, and predictive analytics — trained on your data, deployed to your infrastructure.

Evaluation & observability

AI systems need monitoring too. We build evaluation frameworks, track model performance over time, and alert on quality degradation before users notice.

What you receive
  • AI feature or standalone system
  • Prompt engineering and evaluation framework
  • Vector database and retrieval pipeline
  • Monitoring and cost management setup
  • Integration with existing systems
  • Documentation and team training
Technology stack
OpenAIAnthropicLangChainPythonFastAPIPineconeKafkaAWS SageMaker
How we work

Our process

01

Use case validation

We audit your workflows to identify where AI creates genuine leverage — not just where it's technically possible.

02

Prototype & evaluate

A working prototype in 2–3 weeks, evaluated against real data with measurable quality metrics before we commit to a full build.

03

Pipeline engineering

Production-grade data pipelines, vector stores, and model serving infrastructure built for reliability and cost efficiency.

04

Integration & testing

Deep integration with your existing systems, with comprehensive testing including adversarial inputs and edge cases.

05

Monitoring & iteration

Ongoing evaluation of model performance, cost tracking, and iterative improvement as your data and requirements evolve.

Common questions

Ready to get started?

Tell us about your project and we'll respond within 24 hours.